Abstract

Handwriting is individualistic. The uniqueness of shape and style of
handwriting can be used to identify the significant features in authenticating
the author of writing. The main issue in Writer Identification (WI) domain is
how to acquire these significant features that reflect the author of handwriting.
WI is an active area of research in pattern recognition due to extensive
exchange of paper documents. This research is meant to explore the usage of
feature selection in WI. The purpose of feature selection is to obtain the most
minimal sized subset of features which class distribution is as close as possible
to original class distribution. The three popular methods of feature selection
are filter method, wrapper method, and embedded method, however only
wrapper method will be further explored in this research. This research
focuses on identifying the significant features of word shape by using wrapper
feature selection method prior the identification task. Feature selection is
explored in order to find the most significant features which by is the unique
features of individual's writing. This research also proposes an improved
Sequential Forward Floating Selection method besides the exploration of
significant features for invarianceness of authorship from global shape
features by using various wrapper feature selection methods. The promising
results show that the proposed method is worth to receive further exploration
in identifying the handwritten authorship.